让非技术人员在浏览器里协作训练分类模型。
InFL-UX: A Toolkit for Web-Based Interactive Federated Learning
- 基于浏览器的交互式联邦学习工具，支持多设备上传数据与标注
- 用户可直接在网页上参与模型训练，无需复杂配置
- 适合研究人机协作与去中心化学习的学者与开发者
本文提出 InFL-UX，一个基于浏览器的交互式联邦学习（FL）工具包，旨在将用户贡献无缝融入机器学习流程。InFL-UX 允许跨多个设备的用户在浏览器中上传数据集、定义类别，并协作训练分类模型，利用现代 Web 技术实现。与传统聚焦后端模拟的 FL 工具不同，InFL-UX 提供简洁界面，帮助研究人员探索真实场景下用户如何与联邦学习系统互动。通过强调易用性与去中心化训练，InFL-UX 桥接了联邦学习与交互式机器学习（IML）之间的鸿沟，使非技术用户能主动参与分类任务。
原文摘要 · Abstract (English)
This paper presents InFL-UX, an interactive, proof-of-concept browser-based Federated Learning (FL) toolkit designed to integrate user contributions seamlessly into the machine learning (ML) workflow. InFL-UX enables users across multiple devices to upload datasets, define classes, and collaboratively train classification models directly in the browser using modern web technologies. Unlike traditional FL toolkits, which often focus on backend simulations, InFL-UX provides a simple user interface for researchers to explore how users interact with and contribute to FL systems in real-world, interactive settings. By prioritising usability and decentralised model training, InFL-UX bridges the gap between FL and Interactive Machine Learning (IML), empowering non-technical users to actively participate in ML classification tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。